DeepSet’s Bootstrap-to-Scale Playbook: How Services Enabled Better Product-Market Fit
Most AI infrastructure startups follow a familiar playbook: raise venture capital, build a product, then find customers. But in a recent episode of Category Visionaries, DeepSet founder Malte Pietsch revealed how starting with just €5,000 and a services-first approach led to deeper product insights and stronger market positioning.
The €5,000 Starting Point “We started basically doing professional services. We built custom AI solutions, learning solutions for enterprise customers,” Malte explains. What began as a bootstrapped necessity evolved into a strategic advantage, providing deep market insights before committing to product development.
The Learning Laboratory Rather than viewing services as a distraction from their product vision, DeepSet used customer projects as a learning laboratory. “Be close to your customers. So I think for us, this bootstrapping phase was incredibly valuable because we just learned so much about really pain points of these customers in various companies before we had anything.”
This hands-on experience proved particularly valuable given the nascent state of enterprise AI adoption. “Back then when we actually started, no one talked about llms, few talked about nap back then,” Malte recalls. “And were in this early phase where it was with early signs that it comes easier to use these kind of transformer models.”
The Strategic Pivot Despite the profitability of their services business, DeepSet maintained clarity about their ultimate destination. “We didn’t want to build a professional service company,” Malte emphasizes. This conviction led to a careful balancing act – using services revenue to fund development while gathering insights to inform their product strategy.
The Product Evolution The insights gained from services work directly influenced their product development. Today, DeepSet offers two products: Haystack, an open-source framework for individual developers, and DeepSet Cloud, their commercial platform for enterprise teams.
“We more or less from day one, then thought, okay, what’s the commercial product like? What’s the business model that can really create here to have a sustainable business?” This dual-product strategy emerged from understanding different user personas through their services work.
The Market Validation Their services background provided another crucial advantage: built-in market validation. Rather than building in isolation, they could test assumptions with real customers. This proved particularly valuable during the recent AI hype cycle.
“Last year, I think that was where a lot of this noise happened, where so much new things became possible. So a lot of cool ideas, great ideas, exciting demos,” Malte reflects. “But I think at some point, if you don’t want to become hype and just a bubble, you need to show the value coming out of things.”
The Warning for Technical Founders Malte’s most pointed advice addresses a common pitfall among technical founders: “It’s very risky if you are a tech company, if you build a very deep tech product that you may sometimes focus too long on just the technology because you have this vision, you have this idea, you just need to build it, but then maybe you hit the market too late.”
Their services-first approach prevented this trap, ensuring that product development remained grounded in real customer needs rather than technical possibilities alone.
DeepSet’s journey offers a compelling alternative to the traditional venture-backed startup path. Their story suggests that sometimes the best way to build innovative technology isn’t to start with the technology at all – it’s to start with deep customer understanding and let that guide product development.
For technical founders building in emerging markets, this approach offers a powerful template: use services not just as a funding mechanism, but as a strategic tool to develop the market insights that lead to better product decisions and stronger market positioning.